Intracluster content management using neuro-response priming data

ABSTRACT

A system uses neuro-response information to evaluate content within a cluster, such as commercials in a pod, advertisements in a frame, or products on a shelf, to determine priming characteristics associated with each pieces of content within the cluster. The priming characteristics and other data are combined to obtain blended attributes. The blended attributes are correlated with each piece of intracluster content to allow intelligent management including selection, arrangement, ordering, presentation, and/or scheduling of intracluster content. Intracluster content may also use priming characteristics associated with extracluster content to further improve management.

RELATED APPLICATION

This patent arises from a continuation of U.S. patent application Ser.No. 12/608,696, which was filed on Oct. 29, 2009 and is herebyincorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates to intracluster such as intrapod contentmanagement using neuro-response priming data.

DESCRIPTION OF RELATED ART

Conventional systems for management of intracluster content are limitedor non-existent. Many conventional systems provide somewhat randomizedpresentation of content such as commercials and advertisements includedin a cluster or pod. In some instances, attention may be paid to theprogram content presented before and after a commercial break toidentify appropriate content for association with advertisements orcommercials. However, conventional systems are subject to semantic,syntactic, metaphorical, cultural, and interpretive errors.

Consequently, it is desirable to provide improved mechanisms forintracluster content management.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure may best be understood by reference to the followingdescription taken in conjunction with the accompanying drawings, whichillustrate particular example embodiments.

FIG. 1 illustrates one example of a system for intracluster contentmanagement.

FIG. 2 illustrates examples of stimulus attributes that can be includedin a repository.

FIG. 3 illustrates examples of data models that can be used with astimulus and response repository.

FIG. 4 illustrates one example of a query that can be used with theintracluster content management system.

FIG. 5 illustrates one example of a report generated using theintracluster content management system.

FIG. 6 illustrates one example of a technique for performing dataanalysis.

FIG. 7 illustrates one example of technique for intracluster contentmanagement.

FIG. 8 provides one example of a system that can be used to implementone or more mechanisms.

DESCRIPTION OF PARTICULAR EMBODIMENTS

Reference will now be made in detail to some specific examples of theinvention including the best modes contemplated by the inventors forcarrying out the invention. Examples of these specific embodiments areillustrated in the accompanying drawings. While the invention isdescribed in conjunction with these specific embodiments, it will beunderstood that it is not intended to limit the invention to thedescribed embodiments. On the contrary, it is intended to coveralternatives, modifications, and equivalents as may be included withinthe spirit and scope of the invention as defined by the appended claims.

For example, the techniques and mechanisms of the present invention willbe described in the context of particular types of data such as centralnervous system, autonomic nervous system, and effector data. However, itshould be noted that the techniques and mechanisms of the presentinvention apply to a variety of different types of data. It should benoted that various mechanisms and techniques can be applied to any typeof stimuli. In the following description, numerous specific details areset forth in order to provide a thorough understanding of the presentinvention. Particular example embodiments of the present invention maybe implemented without some or all of these specific details. In otherinstances, well known process operations have not been described indetail in order not to unnecessarily obscure the present invention.

Various techniques and mechanisms of the present invention willsometimes be described in singular form for clarity. However, it shouldbe noted that some embodiments include multiple iterations of atechnique or multiple instantiations of a mechanism unless notedotherwise. For example, a system uses a processor in a variety ofcontexts. However, it will be appreciated that a system can use multipleprocessors while remaining within the scope of the present inventionunless otherwise noted. Furthermore, the techniques and mechanisms ofthe present invention will sometimes describe a connection between twoentities. It should be noted that a connection between two entities doesnot necessarily mean a direct, unimpeded connection, as a variety ofother entities may reside between the two entities. For example, aprocessor may be connected to memory, but it will be appreciated that avariety of bridges and controllers may reside between the processor andmemory. Consequently, a connection does not necessarily mean a direct,unimpeded connection unless otherwise noted.

Overview

A system uses neuro-response information to evaluate content within acluster, such as commercials in a pod, advertisements in a frame, orproducts on a shelf, to determine priming characteristics associatedwith each pieces of content within the cluster. The primingcharacteristics and other data are combined to obtain blendedattributes. The blended attributes are correlated with each piece ofintracluster content to allow intelligent management includingselection, arrangement, ordering, presentation, and/or scheduling ofintracluster content. Intracluster content may also use primingcharacteristics associated with extracluster content to further improvemanagement.

Example Embodiments

Conventional mechanisms for managing intracluster are limited ornon-existent. One problem with conventional mechanisms for selectingadvertising is that they do not measure the inherent message resonanceand priming for various products, services, and offerings that areattributable to the stimulus. They are also prone to semantic,syntactic, metaphorical, cultural, and interpretive errors therebypreventing the accurate and repeatable targeting of the audience.

Conventional systems do not use neuro-behavioral and neuro-physiologicalresponse blended manifestations in assessing the user response and donot elicit an individual customized neuro-physiological and/orneuro-behavioral response to the stimulus. Conventional systems alsofail to blend multiple datasets, and blended manifestations ofmulti-modal responses, across multiple datasets, individuals andmodalities, to reveal and validate the elicited measures of resonanceand priming to allow for intelligent selection of personalized content.

In these respects, the intracluster content management system accordingto the present invention substantially departs from the conventionalconcepts and designs of the prior art. According to various embodiments,it is recognized that a subject commercial or advertisement forparticular products, services, and offerings may be particularlyeffective when a user is primed for the particular products, services,and offerings by other commercials and advertisements in close proximityto the subject commercial or advertisements. For example, anadvertisement for cleaning supplies may be particularly effective forviewers who have viewed an advertisement on antibacterial soap, or anadvertisement for a sports car may be particularly effective for viewerswho have recently viewed a commercial for a NASCAR program in the samecommercial pod. In still other examples, an audio advertisement forpackaged salads may be more effective after listening to an audiadvertisement for a weight loss program in the same audio advertisementcluster.

Consequently, the techniques and mechanisms of the present inventiondetermine priming characteristics of intra cluster content. In someexamples, priming characteristics are blended with user characteristicssuch as interests, location, income level, product likes and dislikes,purchase history, etc. to obtain blended attributes. The blendedattributes may be correlated with intracluster content in order tointelligently manage intracluster content. For example, a company mayelect to place an advertisement for chore type products before anadvertisement for leisure type products upon determining primingcharacteristics of the products within a commercial pod. In someexamples, commercials and advertisements are labeled and tagged to allowfor improved selection and arrangement. In other examples, a company mayplace a printed advertisement for a spa treatment right next to a printadvertisement for a vacation getaway.

Advertisers can assess the value of particular slots within a clustersuch as a commercial pod, advertisement page, or store shelf based onpriming levels and resonance and access to preferred users.

According to various embodiments, the techniques and mechanisms of thepresent invention may use a variety of mechanisms such as survey basedresponses, statistical data, and/or neuro-response measurements such ascentral nervous system, autonomic nervous system, and effectormeasurements to improve intracluster content management. Some examplesof central nervous system measurement mechanisms include FunctionalMagnetic Resonance Imaging (fMRI) and Electroencephalography (EEG). fMRImeasures blood oxygenation in the brain that correlates with increasedneural activity. However, current implementations of fMRI have poortemporal resolution of few seconds. EEG measures electrical activityassociated with post synaptic currents occurring in the millisecondsrange. Subcranial EEG can measure electrical activity with the mostaccuracy, as the bone and dermal layers weaken transmission of a widerange of frequencies. Nonetheless, surface EEG provides a wealth ofelectrophysiological information if analyzed properly. Even portable EEGwith dry electrodes provides a large amount of neuro-responseinformation.

Autonomic nervous system measurement mechanisms include Galvanic SkinResponse (GSR), Electrocardiograms (EKG), pupillary dilation, etc.Effector measurement mechanisms include Electrooculography (EOG), eyetracking, facial emotion encoding, reaction time etc.

According to various embodiments, the techniques and mechanisms of thepresent invention intelligently blend multiple modes and manifestationsof precognitive neural signatures with cognitive neural signatures andpost cognitive neurophysiological manifestations to more accuratelyperform intracluster content management. In some examples, autonomicnervous system measures are themselves used to validate central nervoussystem measures. Effector and behavior responses are blended andcombined with other measures. According to various embodiments, centralnervous system, autonomic nervous system, and effector systemmeasurements are aggregated into a measurement that allows intraclustercontent management.

In particular embodiments, subjects are exposed to stimulus material anddata such as central nervous system, autonomic nervous system, andeffector data is collected during exposure. According to variousembodiments, data is collected in order to determine a resonance measurethat aggregates multiple component measures that assess resonance data.In particular embodiments, specific event related potential (ERP)analyses and/or event related power spectral perturbations (ERPSPs) areevaluated for different regions of the brain both before a subject isexposed to stimulus and each time after the subject is exposed tostimulus.

According to various embodiments, pre-stimulus and post-stimulusdifferential as well as target and distracter differential measurementsof ERP time domain components at multiple regions of the brain aredetermined (DERP). Event related time-frequency analysis of thedifferential response to assess the attention, emotion and memoryretention (DERPSPs) across multiple frequency bands including but notlimited to theta, alpha, beta, gamma and high gamma is performed. Inparticular embodiments, single trial and/or averaged DERP and/or DERPSPscan be used to enhance the resonance measure and determine priminglevels for various products and services.

A variety of stimulus materials such as entertainment and marketingmaterials, media streams, billboards, print advertisements, textstreams, music, performances, sensory experiences, etc. can be analyzed.According to various embodiments, enhanced neuro-response data isgenerated using a data analyzer that performs both intra-modalitymeasurement enhancements and cross-modality measurement enhancements.According to various embodiments, brain activity is measured not just todetermine the regions of activity, but to determine interactions andtypes of interactions between various regions. The techniques andmechanisms of the present invention recognize that interactions betweenneural regions support orchestrated and organized behavior. Attention,emotion, memory, and other abilities are not merely based on one part ofthe brain but instead rely on network interactions between brainregions.

The techniques and mechanisms of the present invention further recognizethat different frequency bands used for multi-regional communication canbe indicative of the effectiveness of stimuli. In particularembodiments, evaluations are calibrated to each subject and synchronizedacross subjects. In particular embodiments, templates are created forsubjects to create a baseline for measuring pre and post stimulusdifferentials. According to various embodiments, stimulus generators areintelligent and adaptively modify specific parameters such as exposurelength and duration for each subject being analyzed.

A variety of modalities can be used including EEG, GSR, EKG, pupillarydilation, EOG, eye tracking, facial emotion encoding, reaction time,etc. Individual modalities such as EEG are enhanced by intelligentlyrecognizing neural region communication pathways. Cross modalityanalysis is enhanced using a synthesis and analytical blending ofcentral nervous system, autonomic nervous system, and effectorsignatures. Synthesis and analysis by mechanisms such as time and phaseshifting, correlating, and validating intra-modal determinations allowgeneration of a composite output characterizing the significance ofvarious data responses to effectively characterize and manageintracluster content.

FIG. 1 illustrates one example of a system for performing intraclustercontent management using central nervous system, autonomic nervoussystem, and/or effector measures. According to various embodiments, theintracluster content management system includes a stimulus presentationdevice 101. In particular embodiments, the stimulus presentation device101 is merely a display, monitor, screen, etc., that displays stimulusmaterial to a user. The stimulus material may be a media clip, acommercial, pages of text, a brand image, a performance, a magazineadvertisement, a movie, an audio presentation, and may even involveparticular tastes, smells, textures and/or sounds. The stimuli caninvolve a variety of senses and occur with or without human supervision.Continuous and discrete modes are supported. According to variousembodiments, the stimulus presentation device 101 also has protocolgeneration capability to allow intelligent customization of stimuliprovided to multiple subjects in different markets.

According to various embodiments, stimulus presentation device 101 couldinclude devices such as televisions, cable consoles, computers andmonitors, projection systems, display devices, speakers, tactilesurfaces, etc., for presenting the stimuli including but not limited toadvertising and entertainment from different networks, local networks,cable channels, syndicated sources, websites, internet contentaggregators, portals, service providers, etc.

According to various embodiments, the subjects 103 are connected to datacollection devices 105. The data collection devices 105 may include avariety of neuro-response measurement mechanisms including neurologicaland neurophysiological measurements systems such as EEG, EOG, GSR, EKG,pupillary dilation, eye tracking, facial emotion encoding, and reactiontime devices, etc. According to various embodiments, neuro-response dataincludes central nervous system, autonomic nervous system, and effectordata. In particular embodiments, the data collection devices 105 includeEEG 111, EOG 113, and GSR 115. In some instances, only a single datacollection device is used. Data collection may proceed with or withouthuman supervision.

The data collection device 105 collects neuro-response data frommultiple sources. This includes a combination of devices such as centralnervous system sources (EEG), autonomic nervous system sources (GSR,EKG, pupillary dilation), and effector sources (EOG, eye tracking,facial emotion encoding, reaction time). In particular embodiments, datacollected is digitally sampled and stored for later analysis. Inparticular embodiments, the data collected could be analyzed inreal-time. According to particular embodiments, the digital samplingrates are adaptively chosen based on the neurophysiological andneurological data being measured.

In one particular embodiment, the intracluster content management systemincludes EEG 111 measurements made using scalp level electrodes, EOG 113measurements made using shielded electrodes to track eye data, GSR 115measurements performed using a differential measurement system, a facialmuscular measurement through shielded electrodes placed at specificlocations on the face, and a facial affect graphic and video analyzeradaptively derived for each individual.

In particular embodiments, the data collection devices are clocksynchronized with a stimulus presentation device 101. In particularembodiments, the data collection devices 105 also include a conditionevaluation subsystem that provides auto triggers, alerts and statusmonitoring and visualization components that continuously monitor thestatus of the subject, data being collected, and the data collectioninstruments. The condition evaluation subsystem may also present visualalerts and automatically trigger remedial actions. According to variousembodiments, the data collection devices include mechanisms for not onlymonitoring subject neuro-response to stimulus materials, but alsoinclude mechanisms for identifying and monitoring the stimulusmaterials. For example, data collection devices 105 may be synchronizedwith a set-top box to monitor channel changes. In other examples, datacollection devices 105 may be directionally synchronized to monitor whena subject is no longer paying attention to stimulus material. In stillother examples, the data collection devices 105 may receive and storestimulus material generally being viewed by the subject, whether thestimulus is a program, a commercial, printed material, or a sceneoutside a window. The data collected allows analysis of neuro-responseinformation and correlation of the information to actual stimulusmaterial and not mere subject distractions.

According to various embodiments, the intracluster content managementsystem also includes a data cleanser device 121. In particularembodiments, the data cleanser device 121 filters the collected data toremove noise, artifacts, and other irrelevant data using fixed andadaptive filtering, weighted averaging, advanced component extraction(like PCA, ICA), vector and component separation methods, etc. Thisdevice cleanses the data by removing both exogenous noise (where thesource is outside the physiology of the subject, e.g. a phone ringingwhile a subject is viewing a video) and endogenous artifacts (where thesource could be neurophysiological, e.g. muscle movements, eye blinks,etc.).

The artifact removal subsystem includes mechanisms to selectivelyisolate and review the response data and identify epochs with timedomain and/or frequency domain attributes that correspond to artifactssuch as line frequency, eye blinks, and muscle movements. The artifactremoval subsystem then cleanses the artifacts by either omitting theseepochs, or by replacing these epoch data with an estimate based on theother clean data (for example, an EEG nearest neighbor weightedaveraging approach).

According to various embodiments, the data cleanser device 121 isimplemented using hardware, firmware, and/or software. It should benoted that although a data cleanser device 121 is shown located after adata collection device 105 and before priming and preference integration181, the data cleanser device 121 like other components may have alocation and functionality that varies based on system implementation.For example, some systems may not use any automated data cleanser devicewhatsoever while in other systems, data cleanser devices may beintegrated into individual data collection devices.

In particular embodiments, an optional survey and interview systemcollects and integrates user survey and interview responses to combinewith neuro-response data to more effectively select content fordelivery. According to various embodiments, the survey and interviewsystem obtains information about user characteristics such as age,gender, income level, location, interests, buying preferences, hobbies,etc. The survey and interview system can also be used to obtain userresponses about particular pieces of stimulus material.

According to various embodiments, the priming repository system 131associates meta-tags with various temporal and spatial locations inintracluster content. In some examples, commercial or advertisement (ad)breaks are provided with a set of meta-tags that identify commercial oradvertising content that would be most suitable for a particularintracluster slot. The slot may be a particular position in a commercialpod or a particular location on a page.

Each slot may identify categories of products and services that areprimed at a particular point in a cluster. The content may also specifythe level of priming associated with each category of product orservice. For example, a first commercial may show an old house andbuildings. Meta-tags may be manually or automatically generated toindicate that commercials for home improvement products would besuitable for a particular advertisement slot or slots following thefirst commercial.

In some instances, meta-tags may include spatial and temporalinformation indicating where and when particular advertisements shouldbe placed. For example, a page that includes advertisements about petadoptions may indicate that a banner advertisement for pet care relatedproducts may be suitable. The advertisements may be separate from aprogram or integrated into a program. According to various embodiments,the priming repository system 131 also identifies scenes elicitingsignificant audience resonance to particular products and services aswell as the level and intensity of resonance. The information in thepriming repository system 131 may be manually or automaticallygenerated. In some examples, the priming repository system 131 has datagenerated by determining resonance characteristics for temporal andspatial locations in various intracluster slots.

An optional personalization repository system provides information aboutparticular users or groups of users. According to various embodiments,the personalization repository system identifies sets of personalpreferences for products and services, audio characteristics, videocharacteristics, length, channel, delivery mode (television, radio,mobile, internet), emotional content, imagery, attentioncharacteristics. The information may be obtained using historicalpurchase behavior, demographic based purchasing profiles, user surveyinputs, or even neuro-response data etc. For example, response data mayshow that a user is particularly interested in apparel advertisements.This may correlate directly with a survey response indicating the sameinterest.

The information from a priming repository system 131 may be combinedwith information from a personalization repository system using apriming and preference blender or integration system 181. According tovarious embodiments, the priming and preference blender weighs andcombines components of priming and personalization characteristics toselect material and/or insertion points for the material. The materialmay be marketing, entertainment, informational, etc., personalized for aparticular user.

In particular embodiments, neuro-response preferences are blended withconscious, indicated, and/or inferred user preferences to selectneurologically effective advertising for presentation to the user. Inone particular example, neuro-response data may indicate that beverageadvertisements would be suitable for a particular advertisement break.User preferences may indicate that a particular viewer prefers dietsodas. An advertisement for a low calorie beverage may be selected andprovided to the particular user. According to various embodiments, a setof weights and functions use a combination of rule based and fuzzy logicbased decision making to determine the areas of maximal overlap betweenthe priming repository system and the personalization repository system.Clustering analysis may be performed to determine clustering of primingbased preferences and personalization based preferences along a commonnormalized dimension, such as a subset or group of individuals. Inparticular embodiments, a set of weights and algorithms are used to mappreferences in the personalization repository to identified maxima forpriming.

According to various embodiments, the intracluster content managementsystem includes a data analyzer associated with the data cleanser 121.The data analyzer uses a variety of mechanisms to analyze underlyingdata in the system to determine resonance. According to variousembodiments, the data analyzer customizes and extracts the independentneurological and neuro-physiological parameters for each individual ineach modality, and blends the estimates within a modality as well asacross modalities to elicit an enhanced response to the presentedstimulus material. In particular embodiments, the data analyzeraggregates the response measures across subjects in a dataset.

According to various embodiments, neurological and neuro-physiologicalsignatures are measured using time domain analyses and frequency domainanalyses. Such analyses use parameters that are common acrossindividuals as well as parameters that are unique to each individual.The analyses could also include statistical parameter extraction andfuzzy logic based attribute estimation from both the time and frequencycomponents of the synthesized response.

In some examples, statistical parameters used in a blended effectivenessestimate include evaluations of skew, peaks, first and second moments,distribution, as well as fuzzy estimates of attention, emotionalengagement and memory retention responses.

According to various embodiments, the data analyzer may include anintra-modality response synthesizer and a cross-modality responsesynthesizer. In particular embodiments, the intra-modality responsesynthesizer is configured to customize and extract the independentneurological and neurophysiological parameters for each individual ineach modality and blend the estimates within a modality analytically toelicit an enhanced response to the presented stimuli. In particularembodiments, the intra-modality response synthesizer also aggregatesdata from different subjects in a dataset.

According to various embodiments, the cross-modality responsesynthesizer or fusion device blends different intra-modality responses,including raw signals and signals output. The combination of signalsenhances the measures of effectiveness within a modality. Thecross-modality response fusion device can also aggregate data fromdifferent subjects in a dataset.

According to various embodiments, the data analyzer also includes acomposite enhanced effectiveness estimator (CEEE) that combines theenhanced responses and estimates from each modality to provide a blendedestimate of the effectiveness. In particular embodiments, blendedestimates are provided for each exposure of a subject to stimulusmaterials. The blended estimates are evaluated over time to assessresonance characteristics. According to various embodiments, numericalvalues are assigned to each blended estimate. The numerical values maycorrespond to the intensity of neuro-response measurements, thesignificance of peaks, the change between peaks, etc. Higher numericalvalues may correspond to higher significance in neuro-responseintensity. Lower numerical values may correspond to lower significanceor even insignificant neuro-response activity. In other examples,multiple values are assigned to each blended estimate. In still otherexamples, blended estimates of neuro-response significance aregraphically represented to show changes after repeated exposure.

According to various embodiments, a data analyzer passes data to aresonance estimator that assesses and extracts resonance patterns. Inparticular embodiments, the resonance estimator determines entitypositions in various stimulus segments and matches position informationwith eye tracking paths while correlating saccades with neuralassessments of attention, memory retention, and emotional engagement. Inparticular embodiments, the resonance estimator stores data in thepriming repository system. As with a variety of the components in thesystem, various repositories can be co-located with the rest of thesystem and the user, or could be implemented in remote locations.

Data from various repositories may be blended and passed to aintracluster content management engine 183. According to variousembodiments, the intracluster content management engine 183 managesintracluster content such as commercials in a pod or advertisements on apage and arranges them to enhance priming and resonance characteristics.Commercials in a pod may be ordered in a particular manner to optimizeeffectiveness. Advertisements on a page may be rearranged to improveviewer response. From a preset category of ads in real time and deliversthe ad that is appropriate for the user through the appropriate deliverychannel and modality. According to various embodiments, the engine 183selects and assembles in a real time, a near real time, or a timedelayed manner intracluster content by associating priming profiles anduser preferences to intracluster content attributes.

FIG. 2 illustrates examples of data models that may be user in aintracluster content management system. According to variousembodiments, a stimulus attributes data model 201 includes a channel203, media type 205, time span 207, audience 209, and demographicinformation 211. A stimulus purpose data model 213 may include intents215 and objectives 217. According to various embodiments, stimuluspurpose data model 213 also includes spatial and temporal information219 about entities and emerging relationships between entities.

According to various embodiments, another stimulus attributes data model221 includes creation attributes 223, ownership attributes 225,broadcast attributes 227, and statistical, demographic and/or surveybased identifiers 229 for automatically integrating theneuro-physiological and neuro-behavioral response with other attributesand meta-information associated with the stimulus.

According to various embodiments, a stimulus priming data model 231includes fields for identifying advertisement breaks 233 and scenes 235that can be associated with various priming levels 237 and audienceresonance measurements 239. In particular embodiments, the data model231 provides temporal and spatial information for ads, scenes, events,locations, etc. that may be associated with priming levels and audienceresonance measurements. In some examples, priming levels for a varietyof products, services, offerings, etc. are correlated with temporal andspatial information in source material such as a movie, billboard,advertisement, commercial, store shelf, etc. In some examples, the datamodel associates with each second of a show a set of meta-tags forpre-break content indicating categories of products and services thatare primed. The level of priming associated with each category ofproduct or service at various insertions points may also be provided.Audience resonance measurements and maximal audience resonancemeasurements for various scenes and advertisement breaks may bemaintained and correlated with sets of products, services, offerings,etc.

The priming and resonance information may be used to select intraclusterstimulus suited for particular levels of priming and resonancecorresponding to identified intracluster slots.

FIG. 3 illustrates examples of data models that can be used for storageof information associated with tracking and measurement of resonance.According to various embodiments, a dataset data model 301 includes anexperiment name 303 and/or identifier, client attributes 305, a subjectpool 307, logistics information 309 such as the location, date, and timeof testing, and stimulus material 311 including stimulus materialattributes.

In particular embodiments, a subject attribute data model 315 includes asubject name 317 and/or identifier, contact information 321, anddemographic attributes 319 that may be useful for review of neurologicaland neuro-physiological data. Some examples of pertinent demographicattributes include marriage status, employment status, occupation,household income, household size and composition, ethnicity, geographiclocation, sex, race. Other fields that may be included in data model 315include subject preferences 323 such as shopping preferences,entertainment preferences, and financial preferences. Shoppingpreferences include favorite stores, shopping frequency, categoriesshopped, favorite brands. Entertainment preferences includenetwork/cable/satellite access capabilities, favorite shows, favoritegenres, and favorite actors. Financial preferences include favoriteinsurance companies, preferred investment practices, bankingpreferences, and favorite online financial instruments. A variety ofproduct and service attributes and preferences may also be included. Avariety of subject attributes may be included in a subject attributesdata model 315 and data models may be preset or custom generated to suitparticular purposes.

According to various embodiments, data models for neuro-feedbackassociation 325 identify experimental protocols 327, modalities included329 such as EEG, EOG, GSR, surveys conducted, and experiment designparameters 333 such as segments and segment attributes. Other fields mayinclude experiment presentation scripts, segment length, segment detailslike stimulus material used, inter-subject variations, intra-subjectvariations, instructions, presentation order, survey questions used,etc. Other data models may include a data collection data model 337.According to various embodiments, the data collection data model 337includes recording attributes 339 such as station and locationidentifiers, the data and time of recording, and operator details. Inparticular embodiments, equipment attributes 341 include an amplifieridentifier and a sensor identifier.

Modalities recorded 343 may include modality specific attributes likeEEG cap layout, active channels, sampling frequency, and filters used.EOG specific attributes include the number and type of sensors used,location of sensors applied, etc. Eye tracking specific attributesinclude the type of tracker used, data recording frequency, data beingrecorded, recording format, etc. According to various embodiments, datastorage attributes 345 include file storage conventions (format, namingconvention, dating convention), storage location, archival attributes,expiry attributes, etc.

A preset query data model 349 includes a query name 351 and/oridentifier, an accessed data collection 353 such as data segmentsinvolved (models, databases/cubes, tables, etc.), access securityattributes 355 included who has what type of access, and refreshattributes 357 such as the expiry of the query, refresh frequency, etc.Other fields such as push-pull preferences can also be included toidentify an auto push reporting driver or a user driven report retrievalsystem.

FIG. 4 illustrates examples of queries that can be performed to obtaindata associated with intracluster content management. According tovarious embodiments, queries are defined from general or customizedscripting languages and constructs, visual mechanisms, a library ofpreset queries, diagnostic querying including drill-down diagnostics,and eliciting what if scenarios. According to various embodiments,subject attributes queries 415 may be configured to obtain data from aneuro-informatics repository using a location 417 or geographicinformation, session information 421 such as testing times and dates,and demographic attributes 419. Demographics attributes includehousehold income, household size and status, education level, age ofkids, etc.

Other queries may retrieve stimulus material based on shoppingpreferences of subject participants, countenance, physiologicalassessment, completion status. For example, a user may query for dataassociated with product categories, products shopped, shops frequented,subject eye correction status, color blindness, subject state, signalstrength of measured responses, alpha frequency band ringers, musclemovement assessments, segments completed, etc. Experimental design basedqueries 425 may obtain data from a neuro-informatics repository based onexperiment protocols 427, product category 429, surveys included 431,and stimulus provided 433. Other fields that may used include the numberof protocol repetitions used, combination of protocols used, and usageconfiguration of surveys.

Client and industry based queries may obtain data based on the types ofindustries included in testing, specific categories tested, clientcompanies involved, and brands being tested. Response assessment basedqueries 437 may include attention scores 439, emotion scores, 441,retention scores 443, and effectiveness scores 445. Such queries mayobtain materials that elicited particular scores.

Response measure profile based queries may use mean measure thresholds,variance measures, number of peaks detected, etc. Group response queriesmay include group statistics like mean, variance, kurtosis, p-value,etc., group size, and outlier assessment measures. Still other queriesmay involve testing attributes like test location, time period, testrepetition count, test station, and test operator fields. A variety oftypes and combinations of types of queries can be used to efficientlyextract data.

FIG. 5 illustrates examples of reports that can be generated. Accordingto various embodiments, client assessment summary reports 501 includeeffectiveness measures 503, component assessment measures 505, andresonance measures 507. Effectiveness assessment measures includecomposite assessment measure(s), industry/category/client specificplacement (percentile, ranking, etc.), actionable grouping assessmentsuch as removing material, modifying segments, or fine tuning specificelements, etc, and the evolution of the effectiveness profile over time.In particular embodiments, component assessment reports includecomponent assessment measures like attention, emotional engagementscores, percentile placement, ranking, etc. Component profile measuresinclude time based evolution of the component measures and profilestatistical assessments. According to various embodiments, reportsinclude the number of times material is assessed, attributes of themultiple presentations used, evolution of the response assessmentmeasures over the multiple presentations, and usage recommendations.

According to various embodiments, client cumulative reports 511 includemedia grouped reporting 513 of all stimulus assessed, campaign groupedreporting 515 of stimulus assessed, and time/location grouped reporting517 of stimulus assessed. According to various embodiments, industrycumulative and syndicated reports 521 include aggregate assessmentresponses measures 523, top performer lists 525, bottom performer lists527, outliers 529, and trend reporting 531. In particular embodiments,tracking and reporting includes specific products, categories,companies, brands.

FIG. 6 illustrates one example of building a priming repository systemfor intracluster content management. At 601, stimulus material isprovided to multiple subjects. According to various embodiments,stimulus includes streaming video and audio. In particular embodiments,subjects view stimulus in their own homes in group or individualsettings. In some examples, verbal and written responses are collectedfor use without neuro-response measurements. In other examples, verbaland written responses are correlated with neuro-response measurements.At 603, subject neuro-response measurements are collected using avariety of modalities, such as EEG, ERP, EOG, GSR, etc. At 605, data ispassed through a data cleanser to remove noise and artifacts that maymake data more difficult to interpret. According to various embodiments,the data cleanser removes EEG electrical activity associated withblinking and other endogenous/exogenous artifacts.

According to various embodiments, data analysis is performed. Dataanalysis may include intra-modality response synthesis andcross-modality response synthesis to enhance effectiveness measures. Itshould be noted that in some particular instances, one type of synthesismay be performed without performing other types of synthesis. Forexample, cross-modality response synthesis may be performed with orwithout intra-modality synthesis.

A variety of mechanisms can be used to perform data analysis. Inparticular embodiments, a stimulus attributes repository is accessed toobtain attributes and characteristics of the stimulus materials, alongwith purposes, intents, objectives, etc. In particular embodiments, EEGresponse data is synthesized to provide an enhanced assessment ofeffectiveness. According to various embodiments, EEG measures electricalactivity resulting from thousands of simultaneous neural processesassociated with different portions of the brain. EEG data can beclassified in various bands. According to various embodiments, brainwavefrequencies include delta, theta, alpha, beta, and gamma frequencyranges. Delta waves are classified as those less than 4 Hz and areprominent during deep sleep. Theta waves have frequencies between 3.5 to7.5 Hz and are associated with memories, attention, emotions, andsensations. Theta waves are typically prominent during states ofinternal focus.

Alpha frequencies reside between 7.5 and 13 Hz and typically peak around10 Hz. Alpha waves are prominent during states of relaxation. Beta waveshave a frequency range between 14 and 30 Hz. Beta waves are prominentduring states of motor control, long range synchronization between brainareas, analytical problem solving, judgment, and decision making Gammawaves occur between 30 and 60 Hz and are involved in binding ofdifferent populations of neurons together into a network for the purposeof carrying out a certain cognitive or motor function, as well as inattention and memory. Because the skull and dermal layers attenuatewaves in this frequency range, brain waves above 75-80 Hz are difficultto detect and are often not used for stimuli response assessment.

However, the techniques and mechanisms of the present inventionrecognize that analyzing high gamma band (kappa-band: Above 60 Hz)measurements, in addition to theta, alpha, beta, and low gamma bandmeasurements, enhances neurological attention, emotional engagement andretention component estimates. In particular embodiments, EEGmeasurements including difficult to detect high gamma or kappa bandmeasurements are obtained, enhanced, and evaluated. Subject and taskspecific signature sub-bands in the theta, alpha, beta, gamma and kappabands are identified to provide enhanced response estimates. Accordingto various embodiments, high gamma waves (kappa-band) above 80 Hz(typically detectable with sub-cranial EEG and/ormagnetoencephalography) can be used in inverse model-based enhancementof the frequency responses to the stimuli.

Various embodiments of the present invention recognize that particularsub-bands within each frequency range have particular prominence duringcertain activities. A subset of the frequencies in a particular band isreferred to herein as a sub-band. For example, a sub-band may includethe 40-45 Hz range within the gamma band. In particular embodiments,multiple sub-bands within the different bands are selected whileremaining frequencies are band pass filtered. In particular embodiments,multiple sub-band responses may be enhanced, while the remainingfrequency responses may be attenuated.

An information theory based band-weighting model is used for adaptiveextraction of selective dataset specific, subject specific, taskspecific bands to enhance the effectiveness measure. Adaptive extractionmay be performed using fuzzy scaling. Stimuli can be presented andenhanced measurements determined multiple times to determine thevariation profiles across multiple presentations. Determining variousprofiles provides an enhanced assessment of the primary responses aswell as the longevity (wear-out) of the marketing and entertainmentstimuli. The synchronous response of multiple individuals to stimulipresented in concert is measured to determine an enhanced across subjectsynchrony measure of effectiveness. According to various embodiments,the synchronous response may be determined for multiple subjectsresiding in separate locations or for multiple subjects residing in thesame location.

Although a variety of synthesis mechanisms are described, it should berecognized that any number of mechanisms can be applied—in sequence orin parallel with or without interaction between the mechanisms.

Although intra-modality synthesis mechanisms provide enhancedsignificance data, additional cross-modality synthesis mechanisms canalso be applied. A variety of mechanisms such as EEG, Eye Tracking, GSR,EOG, and facial emotion encoding are connected to a cross-modalitysynthesis mechanism. Other mechanisms as well as variations andenhancements on existing mechanisms may also be included. According tovarious embodiments, data from a specific modality can be enhanced usingdata from one or more other modalities. In particular embodiments, EEGtypically makes frequency measurements in different bands like alpha,beta and gamma to provide estimates of significance. However, thetechniques of the present invention recognize that significance measurescan be enhanced further using information from other modalities.

For example, facial emotion encoding measures can be used to enhance thevalence of the EEG emotional engagement measure. EOG and eye trackingsaccadic measures of object entities can be used to enhance the EEGestimates of significance including but not limited to attention,emotional engagement, and memory retention. According to variousembodiments, a cross-modality synthesis mechanism performs time andphase shifting of data to allow data from different modalities to align.In some examples, it is recognized that an EEG response will often occurhundreds of milliseconds before a facial emotion measurement changes.Correlations can be drawn and time and phase shifts made on anindividual as well as a group basis. In other examples, saccadic eyemovements may be determined as occurring before and after particular EEGresponses. According to various embodiments, time corrected GSR measuresare used to scale and enhance the EEG estimates of significanceincluding attention, emotional engagement and memory retention measures.

Evidence of the occurrence or non-occurrence of specific time domaindifference event-related potential components (like the DERP) inspecific regions correlates with subject responsiveness to specificstimulus. According to various embodiments, ERP measures are enhancedusing EEG time-frequency measures (ERPSP) in response to thepresentation of the marketing and entertainment stimuli. Specificportions are extracted and isolated to identify ERP, DERP and ERPSPanalyses to perform. In particular embodiments, an EEG frequencyestimation of attention, emotion and memory retention (ERPSP) is used asa co-factor in enhancing the ERP, DERP and time-domain responseanalysis.

EOG measures saccades to determine the presence of attention to specificobjects of stimulus. Eye tracking measures the subject's gaze path,location and dwell on specific objects of stimulus. According to variousembodiments, EOG and eye tracking is enhanced by measuring the presenceof lambda waves (a neurophysiological index of saccade effectiveness) inthe ongoing EEG in the occipital and extra striate regions, triggered bythe slope of saccade-onset to estimate the significance of the EOG andeye tracking measures. In particular embodiments, specific EEGsignatures of activity such as slow potential shifts and measures ofcoherence in time-frequency responses at the Frontal Eye Field (FEF)regions that preceded saccade-onset are measured to enhance theeffectiveness of the saccadic activity data.

GSR typically measures the change in general arousal in response tostimulus presented. According to various embodiments, GSR is enhanced bycorrelating EEG/ERP responses and the GSR measurement to get an enhancedestimate of subject engagement. The GSR latency baselines are used inconstructing a time-corrected GSR response to the stimulus. Thetime-corrected GSR response is co-factored with the EEG measures toenhance GSR significance measures.

According to various embodiments, facial emotion encoding uses templatesgenerated by measuring facial muscle positions and movements ofindividuals expressing various emotions prior to the testing session.These individual specific facial emotion encoding templates are matchedwith the individual responses to identify subject emotional response. Inparticular embodiments, these facial emotion encoding measurements areenhanced by evaluating inter-hemispherical asymmetries in EEG responsesin specific frequency bands and measuring frequency band interactions.The techniques of the present invention recognize that not only areparticular frequency bands significant in EEG responses, but particularfrequency bands used for communication between particular areas of thebrain are significant. Consequently, these EEG responses enhance theEMG, graphic and video based facial emotion identification.

According to various embodiments, post-stimulus versus pre-stimulusdifferential measurements of ERP time domain components in multipleregions of the brain (DERP) are measured at 607. The differentialmeasures give a mechanism for eliciting responses attributable to thestimulus. For example the messaging response attributable to anadvertisement or the brand response attributable to multiple brands isdetermined using pre-resonance and post-resonance estimates

At 609, target versus distracter stimulus differential responses aredetermined for different regions of the brain (DERP). At 611, eventrelated time-frequency analysis of the differential response (DERPSPs)are used to assess the attention, emotion and memory retention measuresacross multiple frequency bands. According to various embodiments, themultiple frequency bands include theta, alpha, beta, gamma and highgamma or kappa. At 613, priming levels and resonance for variousproducts, services, and offerings are determined at different locationsin the stimulus material. In some examples, priming levels and resonanceare manually determined. In other examples, priming levels and resonanceare automatically determined using neuro-response measurements.According to various embodiments, video streams are modified withdifferent inserted advertisements for various products and services todetermine the effectiveness of the inserted advertisements based onpriming levels and resonance of the source material.

At 617, multiple trials are performed to enhance priming and resonancemeasures. In some examples, stimulus. In some examples, multiple trialsare performed to enhance resonance measures.

In particular embodiments, the priming and resonance measures are sentto a priming repository 619. The priming repository 619 may be used toautomatically select and place advertising suited for particular slotsin a cluster. Commercials in a pod may be automatically ordered orarranged to increase effectiveness.

FIG. 7 illustrates an example of a technique for intracluster contentmanagement. At 701, priming characteristics of intracluster content aredetermined. According to various embodiments, priming characteristics ofintracluster as well as intercluster content are determined.Intercluster content may include regular programming, text articles,program content, etc. Intercluster content may include metatagsindicating the level of priming for various products, services, andofferings. At 703, preference characteristics may be determined. In someimplementations, preference characteristics are not used. Userpreferences including user profile information and attributes may beobtained from a personalization repository system.

In particular embodiments, the user preferences may identify userinterests, purchase patterns, location, income level, gender, preferredproducts and services, etc. At 705, priming and preference informationis blended. According to various embodiments, priming and preferenceattributes are weighted and blended to allow selection and arrangementof neurologically effective intracluster content for individual users.In particular embodiments, priming may indicate that apparel relatedcontent would be effective after accessory related content in a clusterof advertisements.

At 707, blended attributes are used to select, order, and arrangecontent having attributes closely correlated with blended priming andpreference attributes for intracluster slots. According to variousembodiments, attributes derived from blending priming and preferenceinformation is correlated with stimulus material attributes. Inparticular embodiments, content having the strongest correlation forparticular slots is selected for those particular slots. At 709, contentis presented in intracluster slots to a user.

According to various embodiments, various mechanisms such as the datacollection mechanisms, the intra-modality synthesis mechanisms,cross-modality synthesis mechanisms, etc. are implemented on multipledevices. However, it is also possible that the various mechanisms beimplemented in hardware, firmware, and/or software in a single system.FIG. 8 provides one example of a system that can be used to implementone or more mechanisms. For example, the system shown in FIG. 8 may beused to implement a resonance measurement system.

According to particular example embodiments, a system 800 suitable forimplementing particular embodiments of the present invention includes aprocessor 801, a memory 803, an interface 811, and a bus 815 (e.g., aPCI bus). When acting under the control of appropriate software orfirmware, the processor 801 is responsible for such tasks such aspattern generation. Various specially configured devices can also beused in place of a processor 801 or in addition to processor 801. Thecomplete implementation can also be done in custom hardware. Theinterface 811 is typically configured to send and receive data packetsor data segments over a network. Particular examples of interfaces thedevice supports include host bus adapter (HBA) interfaces, Ethernetinterfaces, frame relay interfaces, cable interfaces, DSL interfaces,token ring interfaces, and the like.

According to particular example embodiments, the system 800 uses memory803 to store data, algorithms and program instructions. The programinstructions may control the operation of an operating system and/or oneor more applications, for example. The memory or memories may also beconfigured to store received data and process received data.

Because such information and program instructions may be employed toimplement the systems/methods described herein, the present inventionrelates to tangible, machine readable media that include programinstructions, state information, etc. for performing various operationsdescribed herein. Examples of machine-readable media include, but arenot limited to, magnetic media such as hard disks, floppy disks, andmagnetic tape; optical media such as CD-ROM disks and DVDs;magneto-optical media such as optical disks; and hardware devices thatare specially configured to store and perform program instructions, suchas read-only memory devices (ROM) and random access memory (RAM).Examples of program instructions include both machine code, such asproduced by a compiler, and files containing higher level code that maybe executed by the computer using an interpreter.

Although the foregoing invention has been described in some detail forpurposes of clarity of understanding, it will be apparent that certainchanges and modifications may be practiced within the scope of theappended claims. Therefore, the present embodiments are to be consideredas illustrative and not restrictive and the invention is not to belimited to the details given herein, but may be modified within thescope and equivalents of the appended claims.

1. A method, comprising: analyzing first neuro-response data obtainedfrom a subject exposed to a first advertisement; analyzing secondneuro-response data obtained from the subject exposed to a secondadvertisement; using a processor to determine a first primingcharacteristic associated with the first advertisement based on thefirst neuro-response data and to determine a second primingcharacteristic associated with the second advertisement based on thesecond neuro-response data; selecting a first position from a firstplurality of positions in a first advertisement break for placement ofthe first advertisement; and selecting a second position from the firstplurality of positions in the first advertisement break for placement ofthe second advertisement based on at least one of the first primingcharacteristic or the second priming characteristic.
 2. The method ofclaim 1 further comprising determining a first resonance to the firstadvertisement based on the first neuro-response data and a secondresonance to the second advertisement based on the second neuro-responsedata.
 3. The method of claim 2 further comprising selecting the firstposition based on the first resonance and selecting the second positionbased on the second priming characteristic and the second resonance. 4.The method of claim 2 further comprising determining the first resonancebased on one or more of an event related potential or an event relatedpower spectral perturbation.
 5. The method of claim 2 further comprisingdetermining the first resonance based on a differential measurement ofevent related potential time domain components at multiple regions of abrain of the subject.
 6. The method of claim 2 further comprisingdetermining the first resonance based on event related time-frequencyanalysis of a differential response.
 7. The method of claim 1 furthercomprising basing the first priming characteristic on thirdneuro-response data obtained from the subject while exposed toentertainment preceding the first advertisement.
 8. A system,comprising: a processor to: analyze first neuro-response data obtainedfrom a subject exposed to a first advertisement; analyze secondneuro-response data obtained from the subject exposed to a secondadvertisement; determine a first priming characteristic associated withthe first advertisement based on the first neuro-response data; anddetermine a second priming characteristic associated with the secondadvertisement based on the second neuro-response data; and a selectorto: select a first position from a first plurality of positions in afirst advertisement break or from a second plurality of positions in asecond advertisement break for placement of the first advertisementbased on the first priming characteristic; and select a second positionfrom the first plurality of positions in the first advertisement breakor from the second plurality of positions in the second advertisementbreak for placement of the second advertisement based on the secondpriming characteristic.
 9. The system of claim 8, wherein the firstposition and the second position are in different advertisement breaks.10. The system of claim 8, wherein the processor is to determine a firstresonance to the first advertisement based on the first neuro-responsedata and a second response to the second advertisement based on thesecond neuro-response data.
 11. The system of claim 10, wherein theselector is to select the first position based on the first primingcharacteristic and the first resonance and selecting the second positionbased on the second priming characteristic and the second resonance. 12.The system of claim 10, wherein the processor is to determine the firstresonance based on one or more of an event related potential or an eventrelated power spectral perturbation.
 13. The system of claim 10, whereinthe processor is to determine the first resonance based on adifferential measurement of event related potential time domaincomponents at multiple regions of a brain of the subject.
 14. The systemof claim 10, wherein the processor is to determine the first resonancebased on event related time-frequency analysis of a differentialresponse.
 15. The system of claim 10, wherein the processor is to basethe first priming characteristic on third neuro-response data obtainedfrom the subject while exposed to entertainment preceding the firstadvertisement.
 16. A computer readable storage medium comprisinginstructions, which when executed cause a machine to at least: analyzefirst neuro-response data obtained from a subject exposed to a firstadvertisement; analyze second neuro-response data obtained from thesubject exposed to a second advertisement; determine a first primingcharacteristic associated with the first advertisement based on thefirst neuro-response data; determine a second priming characteristicassociated with the second advertisement based on the secondneuro-response data; select a first position from a first plurality ofpositions in a first advertisement break for placement of the firstadvertisement; and select a second position from the first plurality ofpositions in the first advertisement break for placement of the secondadvertisement based on at least one of the first priming characteristicor the second priming characteristic.
 17. The medium of claim 16,wherein the instructions further cause the machine to determine a firstresonance to the first advertisement based on the first neuro-responsedata and a second resonance to the second advertisement based on thesecond neuro-response data.
 18. The medium of claim 17, wherein theinstructions further cause the machine to determine the first resonancebased on one or more of an event related potential or an event relatedpower spectral perturbation.
 19. The medium of claim 17, wherein theinstructions further cause the machine to determine the first resonancebased on a differential measurement of event related potential timedomain components at multiple regions of a brain of the subject.
 20. Themedium of claim 17, wherein the instructions further cause the machineto determine the first resonance based on event related time-frequencyanalysis of a differential response.